code: def get_T5_layer_self_attention_forward():
      from transformers.models.t5.modeling_t5 import (
          T5LayerSelfAttention,
      )
  
      def forward(
          self: T5LayerSelfAttention,
          hidden_states: torch.Tensor,
          attention_mask: Optional[torch.Tensor] = None,
          position_bias: Optional[torch.Tensor] = None,
          layer_head_mask: Optional[torch.Tensor] = None,
          past_key_value: Optional[Tuple[torch.Tensor]] = None,
          use_cache: bool = False,
          output_attentions: bool = False,
      ) -> Tuple[
          torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]
      ]:
          normed_hidden_states = self.layer_norm(hidden_states)
          attention_output = self.SelfAttention(
              normed_hidden_states,
              mask=attention_mask,
              position_bias=position_bias,
              layer_head_mask=layer_head_mask,
              past_key_value=past_key_value,
              use_cache=use_cache,
              output_attentions=output_attentions,
          )
          hidden_states = self.dropout_add(
              attention_output[0],
              hidden_states,
              self.dropout.p,
              self.dropout.training,
          )
          outputs = (hidden_states,) + attention_output[
              1:
          ]  # add attentions if we output them
          return outputs
  
      return forward
  
file_path: colossalai/colossalai/shardformer/modeling/t5.py
imports: import warnings
  from typing import Dict, List, Optional, Tuple, Union
  import torch
  from torch.nn import CrossEntropyLoss
  from torch.utils.checkpoint import checkpoint
  from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput
  from transformers.models.t5.modeling_t5 import T5EncoderModel, T5ForConditionalGeneration, T5Model, T5Stack
  from transformers.utils import logging
  from colossalai.pipeline.stage_manager import PipelineStageManager
label_desc: Use image node_id 3 for calling a global function with example usage: get_T5_layer_self_attention_forward() and returns: forward, outputs